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app.py

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+import gradio as gr
+import torch
+import torch.nn.functional as F
+from torchvision import transforms, models
+from PIL import Image
+import json
+
+# Load model metadata
+CLASS_NAMES = [
+    "Crossbite",
+    "Crowding", 
+    "Deepbite",
+    "No Treatment Needed",
+    "Open Bite",
+    "Overbite",
+    "Spacing",
+    "Underbite"
+]
+
+# Condition descriptions
+DESCRIPTIONS = {
+    "Crossbite": "A misalignment where upper teeth bite inside lower teeth.",
+    "Crowding": "Insufficient space causing teeth to overlap or twist.",
+    "Deepbite": "Upper front teeth excessively overlap lower front teeth.",
+    "No Treatment Needed": "Teeth appear to be properly aligned.",
+    "Open Bite": "Upper and lower teeth don\'t touch when mouth is closed.",
+    "Overbite": "Upper front teeth protrude significantly over lower teeth.",
+    "Spacing": "Gaps or spaces between teeth.",
+    "Underbite": "Lower teeth protrude beyond upper teeth."
+}
+
+# Load model
+print("Loading model...")
+model = models.resnet18(weights=None)
+model.fc = torch.nn.Linear(model.fc.in_features, 8)
+state_dict = torch.load("pytorch_model.pth", map_location="cpu", weights_only=True)
+model.load_state_dict(state_dict)
+model.eval()
+print("Model loaded!")
+
+# Image preprocessing
+transform = transforms.Compose([
+    transforms.Resize((512, 512)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+def predict(image):
+    """Predict orthodontic condition from image"""
+    if image is None:
+        return {"error": "No image provided"}
+    
+    # Convert to PIL if needed
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
+    
+    image = image.convert("RGB")
+    
+    # Preprocess
+    img_tensor = transform(image).unsqueeze(0)
+    
+    # Predict
+    with torch.no_grad():
+        outputs = model(img_tensor)
+        probabilities = F.softmax(outputs, dim=1)[0]
+        confidence, predicted_idx = torch.max(probabilities, 0)
+    
+    predicted_condition = CLASS_NAMES[predicted_idx.item()]
+    confidence_pct = confidence.item() * 100
+    
+    # Get all probabilities
+    all_probs = {CLASS_NAMES[i]: float(probabilities[i].item() * 100) 
+                 for i in range(len(CLASS_NAMES))}
+    
+    # Determine recommendation
+    if predicted_condition == "No Treatment Needed":
+        recommendation = "not_candidate"
+        recommendation_text = "Based on the AI analysis, you may not need orthodontic treatment at this time."
+    elif confidence_pct >= 70:
+        recommendation = "candidate"
+        recommendation_text = f"You appear to be a good candidate for orthodontic treatment to address {predicted_condition.lower()}."
+    else:
+        recommendation = "requires_evaluation"
+        recommendation_text = "We recommend scheduling a consultation with an orthodontist for a thorough evaluation."
+    
+    return {
+        "predicted_condition": predicted_condition,
+        "confidence": round(confidence_pct, 2),
+        "all_probabilities": {k: round(v, 2) for k, v in all_probs.items()},
+        "recommendation": recommendation,
+        "recommendation_text": recommendation_text,
+        "condition_description": DESCRIPTIONS.get(predicted_condition, ""),
+        "model_version": "ResNet18_512x512",
+        "training_accuracy": 72.73
+    }
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Dental Photo"),
+    outputs=gr.JSON(label="Prediction Results"),
+    title="Orthodontic Condition Classifier",
+    description="Upload a photo of teeth to get an AI-powered orthodontic screening. This is for informational purposes only and should not replace professional evaluation.",
+    examples=None,
+    api_name="predict"
+)
+
+if __name__ == "__main__":
+    demo.launch()